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/*! \file
    \brief
   Default kernel-level fused activation's scale+bias+relu and implicit GEMM
   convolution definitions that combine threadblock-scoped matrix multiply-add
   with the appropriate threadblock-scoped epilogue.
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/conv/kernel/default_conv2d.h"

#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_analytic.h"
#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_analytic.h"
#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_optimized.h"
#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_optimized.h"
#include "cutlass/conv/threadblock/predicated_scale_bias_vector_access_iterator.h"
#include "cutlass/conv/threadblock/regular_scale_bias_vector_access_iterator.h"
#include "cutlass/conv/warp/conv2d_fprop_scale_bias_iterator.h"

/////////////////////////////////////////////////////////////////////////////////////////////////

namespace cutlass {
namespace conv {
namespace kernel {

/////////////////////////////////////////////////////////////////////////////////////////////////
/// Defines a kernel for fused batch norm and Conv2dFprop
template <typename ElementA, typename LayoutA, typename ElementB,
          typename LayoutB, typename ElementScaleBias, typename LayoutScaleBias,
          typename ElementC, typename LayoutC, typename ElementAccumulator,
          typename OperatorClass, typename ArchTag, typename ThreadblockShape,
          typename WarpShape, typename InstructionShape,
          typename EpilogueOutputOp, typename ThreadblockSwizzle, int Stages,
          typename MathOperatorTag,
          conv::IteratorAlgorithm IteratorAlgorithm =
                  IteratorAlgorithm::kOptimized,
          conv::StrideSupport StrideSupport = StrideSupport::kStrided>
struct DefaultConv2dFpropFusion;

/////////////////////////////////////////////////////////////////////////////////////////////////
//                         OpClassTensorOp convolutions
/////////////////////////////////////////////////////////////////////////////////////////////////

/// Defines a kernel for Conv2dFprop specialzation for Analytic
/// IteratorAlgorithm and multistage pipeline.
template <typename ElementA, typename LayoutA, typename ElementB,
          typename LayoutB, typename ElementScaleBias, typename LayoutScaleBias,
          typename ElementC, typename LayoutC, typename ElementAccumulator,
          typename ArchTag, typename ThreadblockShape, typename WarpShape,
          typename InstructionShape, typename EpilogueOutputOp,
          typename ThreadblockSwizzle, int Stages, typename MathOperatorTag>
struct DefaultConv2dFpropFusion<
        ElementA, LayoutA, ElementB, LayoutB, ElementScaleBias, LayoutScaleBias,
        ElementC, LayoutC, ElementAccumulator, arch::OpClassTensorOp, ArchTag,
        ThreadblockShape, WarpShape, InstructionShape, EpilogueOutputOp,
        ThreadblockSwizzle, Stages, MathOperatorTag,
        IteratorAlgorithm::kAnalytic> {
    // Define the core components from GEMM
    using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
            ThreadblockShape, WarpShape, InstructionShape, ElementA,
            layout::RowMajor, ElementB, layout::ColumnMajor, ElementAccumulator,
            layout::RowMajor, arch::OpClassTensorOp, Stages, MathOperatorTag>;

    // Define iterators over tiles from the A operand
    using ThreadMapA = typename MmaCore::IteratorThreadMapA;
    using IteratorA = cutlass::conv::threadblock::
            Conv2dFpropActivationTileAccessIteratorAnalytic<
                    cutlass::MatrixShape<ThreadblockShape::kM,
                                         ThreadblockShape::kK>,
                    ElementA, LayoutA, ThreadMapA>;

    using SmemIteratorA = typename MmaCore::SmemIteratorA;

    // Define iterators over tiles from the B operand
    using ThreadMapB = typename MmaCore::IteratorThreadMapB;
    using IteratorB = cutlass::conv::threadblock::
            Conv2dFpropFilterTileAccessIteratorAnalytic<
                    cutlass::MatrixShape<ThreadblockShape::kK,
                                         ThreadblockShape::kN>,
                    ElementB, LayoutB, ThreadMapB>;

    using SmemIteratorB = typename MmaCore::SmemIteratorB;

    /// Define iterators over tiles from scale/bias vectors
    using IteratorScaleBias =
            cutlass::conv::threadblock::PredicatedScaleBiasVectorAccessIterator<
                    cutlass::MatrixShape<1, ThreadblockShape::kK>,
                    ElementScaleBias, LayoutScaleBias>;

    using SmemIteratorScaleBias =
            cutlass::conv::threadblock::RegularScaleBiasVectorAccessIterator<
                    cutlass::MatrixShape<1, ThreadblockShape::kK>,
                    ElementScaleBias, LayoutScaleBias>;

    // Warp-level GEMM components
    using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
    using MmaPolicy = typename MmaCore::MmaPolicy;

    static int const kThreadCount = 32;

    // Warp-level iterators to load scale and bias vectors
    using WarpIteratorScaleBias = cutlass::conv::warp::WarpIteratorScaleBias<
            MatrixShape<WarpShape::kM, WarpShape::kK>, ElementScaleBias,
            LayoutScaleBias,
            MatrixShape<InstructionShape::kM, InstructionShape::kK>,
            typename WarpMmaTensorOp::IteratorA::Base::Policy, kThreadCount,
            MmaCore::WarpCount::kK>;

    // Define the Mma
    using Mma = threadblock::ImplicitGemmFpropFusionMultistage<
            ThreadblockShape, IteratorA, SmemIteratorA,
            arch::CacheOperation::Always, IteratorB, SmemIteratorB,
            arch::CacheOperation::Global, IteratorScaleBias,
            SmemIteratorScaleBias, arch::CacheOperation::Always, MmaPolicy,
            WarpIteratorScaleBias, Stages>;

    // Define the epilogue
    using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
            ThreadblockShape, WarpMmaTensorOp, 1, EpilogueOutputOp,
            EpilogueOutputOp::kCount>::Epilogue;

    // Define the kernel
    using Kernel = cutlass::conv::kernel::ImplicitGemmConvolutionFusion<
            Mma, Epilogue, ThreadblockSwizzle, conv::Operator::kFprop>;
};

/////////////////////////////////////////////////////////////////////////////////////////////////

/// Defines a kernel for Conv2dFprop specialzation for Optimzed
/// IteratorAlgorithm and multistage pipeline.
template <typename ElementA, typename LayoutA, typename ElementB,
          typename LayoutB, typename ElementScaleBias, typename LayoutScaleBias,
          typename ElementC, typename LayoutC, typename ElementAccumulator,
          typename ArchTag, typename ThreadblockShape, typename WarpShape,
          typename InstructionShape, typename EpilogueOutputOp,
          typename ThreadblockSwizzle, int Stages, typename MathOperatorTag>
struct DefaultConv2dFpropFusion<
        ElementA, LayoutA, ElementB, LayoutB, ElementScaleBias, LayoutScaleBias,
        ElementC, LayoutC, ElementAccumulator, arch::OpClassTensorOp, ArchTag,
        ThreadblockShape, WarpShape, InstructionShape, EpilogueOutputOp,
        ThreadblockSwizzle, Stages, MathOperatorTag,
        IteratorAlgorithm::kOptimized> {
    // Define the core components from GEMM
    using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
            ThreadblockShape, WarpShape, InstructionShape, ElementA,
            layout::RowMajor, ElementB, layout::ColumnMajor, ElementAccumulator,
            layout::RowMajor, arch::OpClassTensorOp, Stages, MathOperatorTag>;

    // Define iterators over tiles from the A operand
    using ThreadMapA = typename MmaCore::IteratorThreadMapA;
    using IteratorA = cutlass::conv::threadblock::
            Conv2dFpropActivationTileAccessIteratorOptimized<
                    cutlass::MatrixShape<ThreadblockShape::kM,
                                         ThreadblockShape::kK>,
                    ElementA, LayoutA, ThreadMapA>;

    using SmemIteratorA = typename MmaCore::SmemIteratorA;

    // Define iterators over tiles from the B operand
    using ThreadMapB = typename MmaCore::IteratorThreadMapB;
    using IteratorB = cutlass::conv::threadblock::
            Conv2dFpropFilterTileAccessIteratorOptimized<
                    cutlass::MatrixShape<ThreadblockShape::kK,
                                         ThreadblockShape::kN>,
                    ElementB, LayoutB, ThreadMapB>;

    using SmemIteratorB = typename MmaCore::SmemIteratorB;

    /// Define iterators over tiles from scale/bias vectors
    using IteratorScaleBias =
            cutlass::conv::threadblock::PredicatedScaleBiasVectorAccessIterator<
                    cutlass::MatrixShape<1, ThreadblockShape::kK>,
                    ElementScaleBias, LayoutScaleBias>;

    using SmemIteratorScaleBias =
            cutlass::conv::threadblock::RegularScaleBiasVectorAccessIterator<
                    cutlass::MatrixShape<1, ThreadblockShape::kK>,
                    ElementScaleBias, LayoutScaleBias>;

    // Warp-level GEMM components
    using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
    using MmaPolicy = typename MmaCore::MmaPolicy;

    static int const kThreadCount = 32;

    // Warp-level iterators to load scale and bias vectors
    using WarpIteratorScaleBias = cutlass::conv::warp::WarpIteratorScaleBias<
            MatrixShape<WarpShape::kM, WarpShape::kK>, ElementScaleBias,
            LayoutScaleBias,
            MatrixShape<InstructionShape::kM, InstructionShape::kK>,
            typename WarpMmaTensorOp::IteratorA::Base::Policy, kThreadCount,
            MmaCore::WarpCount::kK>;

    // Define the Mma
    using Mma = threadblock::ImplicitGemmFpropFusionMultistage<
            ThreadblockShape, IteratorA, SmemIteratorA,
            arch::CacheOperation::Always, IteratorB, SmemIteratorB,
            arch::CacheOperation::Global, IteratorScaleBias,
            SmemIteratorScaleBias, arch::CacheOperation::Always, MmaPolicy,
            WarpIteratorScaleBias, Stages>;

    // Define the epilogue
    using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
            ThreadblockShape, WarpMmaTensorOp, 1, EpilogueOutputOp,
            EpilogueOutputOp::kCount>::Epilogue;

    // Define the kernel
    using Kernel = cutlass::conv::kernel::ImplicitGemmConvolutionFusion<
            Mma, Epilogue, ThreadblockSwizzle, conv::Operator::kFprop>;
};

/////////////////////////////////////////////////////////////////////////////////////////////////

}  // namespace kernel
}  // namespace conv
}  // namespace cutlass

/////////////////////////////////////////////////////////////////////////////////////////////////
